AI is rapidly reshaping manufacturing — from sourcing and design to maintenance, safety, quality, logistics, and customer relationships.
Stephen Gold
President and CEO, Manufacturers Alliance
Most public discourse around AI has centered on chatbots to generate summaries and analyses. Much less discussed are other emerging AI tools that are launching manufacturing into an entirely new realm not envisioned just a decade ago.
Here are just a handful of the dozens of areas on the manufacturing value stream that are already seeing progress in AI implementation:
- Supply chain management: After manufacturers experienced serious sourcing challenges during the pandemic, this ranks as one of the most important functional areas for AI initiatives. Intelligent supplier selection is being used to help evaluate a supplier’s pricing as well as resiliency based on current market data. For global shortages of parts and components, AI is used for alternate material optimization to find new sources. And AI is being used for raw material optimization to help steel manufacturers determine how to use their own scrap in the most efficient way.
- Design and engineering: Manufacturers are speeding up the innovation process and reducing time to market through various AI tools. This includes engineers using AI to review their entire R&D portfolio, then systematically applying AI to advance R&D efficiency throughput and deliverables, including reducing the time to develop commercially viable products. Companies also see design for regulatory compliance as another promising application for AI.
- Equipment maintenance: AI is playing a role in providing detailed instructions. This not only speeds up remediation but enables less experienced staff to address maintenance problems. Some companies use AI to help automation engineers generate code for programmable logic controllers more efficiently, enabling a faster process with less effort and fewer errors. And AI-enabled predictive maintenance is raising the bar on how assets can be protected in an industrial environment. For example, AI can monitor robot health as well as predict failures that might happen before scheduled maintenance.
- Worker safety: AI-based visual systems are being used to address safety evaluation from personal protective equipment compliance to employee fatigue. Especially valuable are AI-powered remote safety inspections in locations where EHS managers are not physically present on site.
- Quality control: With generative AI, analysis of images is helping reduce false positives (good products that fail inspection) as well as false negatives (bad products that pass inspection), thus increasing throughput and quality.
- Warehousing: AI is enabling manufacturers to solve warehousing, inventory, and logistics challenges. Many manufacturers are starting to use AI for inventory optimization. Because price increases can result in a spike in orders followed quickly by a backlog, manufacturers use AI for inventory forecasting. And to ensure more logistical efficiency, such as with their delivery trucks, manufacturers are using generative AI for dynamic load matching.
- Customer relations: Finally, AI can play an important role in building better customer relations by giving manufacturers insights through connected product diagnostics and always-on self-service assistants. This can range from traditional technical support getting faster and better through virtual expert assistants, to aftermarket predictive maintenance that offers the ability to predict when a part might fail based on data they capture from the equipment or vehicle itself.
These and hundreds of other AI applications are effectively ensuring the realization of “industry 4.0.”
